Recent development and wide spread use of medical image capturing apparatuses for Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) have made it possible to obtain a large volume of high-definition digital images for medical use. Furthermore, medical images already interpreted by doctors are increasingly accumulated one by one together with the image interpretation reports thereof in Picture Archiving and Communication Systems (PACS). In order to interpret a target image with reference to medical images similar to the target image, a start is made for development of techniques for searching out the similar images (medical images) from already-accumulated past cases.
How to select appropriate image feature quantities used to determine the similarity between medical images is important in similar image searches. A technique disclosed in the form of a conventional image searching apparatus is described below.
Image feature quantities for determining the similarity between the medical images should vary depending on the kinds of diseases, the progress (stages) of the diseases or the seriousness of the diseases, and the like. However, such conventional medical image searches use the same image feature quantities irrespective of the statuses of the diseases. Non-patent Literature 1 proposes a searching approach composed of two steps that are “customized-queries” approach (CQA) as a means for solving this problem. The first step of this approach is to classify query images using image feature quantities for classifying the classes of the kinds of diseases, the progress of the diseases or the seriousness of the diseases, and the like in the optimum manner. The second step of this approach is to search similar images using the image feature quantities optimized for further classification of the cases included in each of the classes obtained as a result of the previous classification. At this time, the image feature quantities optimum for the classes are calculated in advance through unsupervised learning. Furthermore, the technique disclosed in the Non-patent Literature 1 applies CQA for a lung CT images and to thereby achieve a search recall factor increased from those obtainable in such conventional similar image searches using only a single kind of image feature quantities.